• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.589-596
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• 2022

This study aims to investigate the effect of an educational gamification-based quiz on the recall of formulaic sequences (FS). The experiment involved 87 freshmen enrolled in general English classes at a university in Seoul. As material, EFL textbooks based on content from popular franchises, such as the Marvel Cinematic Universe, Twilight, and Harry Potter, were used. The experiment was carried out as follows: first, vocabulary learning, second, reading comprehension, and third, writing. The fourth activity proceeded differently in two groups. The experimental group used gamification-based quiz to practice FS, whereas the comparison group summarized the reading. FS was evaluated using meaning recall and form recall. Consequently, no difference was found between the groups on meaning recall tests of FS, but the experimental group had a significantly higher average score than the comparison group on the post-test on the form recall of FS.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.3
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• pp.491-510
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• 2014

As a major factor for efficient management and retrieval of the articles in databases, keywords are classified into uncontrolled keywords and controlled keywords. Most of Korean scholarly databases fail to provide controlled vocabularies to indexing research articles which help users to retrieve relevant papers exhaustively. In this paper, we carried out automatic descriptor assignment experiments to Korean articles using automatic classifiers learned with descriptors in international database. The results of the experiments show that the classifier learning with descriptors in international database can potentially offer controlled vocabularies to Korean scholarly articles having English s. Also, we sought to improve the performance of automatic descriptor assignment using various classifiers and combination of them.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.247-271
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• 2019

Uncertainty in scientific knowledge means an uncertain state where propositions are neither true or false at present. The existing studies have analyzed the propositions written in the academic literature, and have conducted the performance evaluation based on the rule based and machine learning based approaches by using the corpus. Although they recognized that the importance of word construction, there are insufficient attempts to expand the word by analyzing the meaning of uncertainty words. On the other hand, studies for analyzing the structure of networks by using bibliometrics and text mining techniques are widely used as methods for understanding intellectual structure and relationship in various disciplines. Therefore, in this study, semantic relations were analyzed by applying Word2Vec to existing uncertainty words. In addition, WordNet, which is an English vocabulary database and thesaurus, was applied to perform a network analysis based on hypernyms, hyponyms, and synonyms relations linked to uncertainty words. The semantic and lexical relationships of uncertainty words were structurally identified. As a result, we identified the possibility of automatically expanding uncertainty words.